Method for computing an energy efficient route

ABSTRACT

Probe data is analyzed to derive Longitudinal Speed Profiles (LSPs) and an Optimal Longitudinal Speed Profile ( 18 ) for each road segment or link in a digital map network. The Longitudinal Speed Profiles (LSPs) profiles are calculated during defined time spans whereas the Optimal Longitudinal Speed Profile ( 18 ) is based on the LSP for the time span corresponding only to free flow traffic conditions. All of the LSPs can used to create a respective energy cost for each time span, or only the OLSP ( 18 ) can be used (or alternatively the RRDSL  16  or LRRDSL  17 ) to calculate an energy cost for the free flow conditions only. The energy cost can be used to predict the energy required by a vehicle to traverse the link Navigation software can use the energy cost to plan the most energy efficient route between two locations in the digital map. Sensory signals can be activated if a driver strays from the Optimal Longitudinal Speed Profile ( 18 ) to achieve extremely high levels of energy efficiency.

STATEMENT OF COPYRIGHTED MATERIAL

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the PTO patent file orrecords, but otherwise reserves all copyright rights whatsoever.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to digital maps of the type for displaying roador pathway information, and more particularly toward a method forsupplementing a digital map with data to enable various traffic modelingactions and to calculate an energy efficient route that can be offeredto a driver.

2. Related Art

Personal navigation devices like that shown generally at 10 in FIG. 1,for example, utilize digital maps combined with accurate positioningdata from GPS or other data streams. These devices 10 have beendeveloped for commuters seeking navigation assistance, for businessestrying to minimize transportation costs, and many other usefulapplications. The effectiveness of such navigation systems is inherentlydependent upon the accuracy and completeness of the information providedto it in the forms of digital maps and associated features and attributedata. Likewise, the effectiveness of such navigation systems is alsodependent upon accurately and quickly matching the actual, real-worldlocation of the navigation device to a corresponding portion of thedigital map. Typically, a navigation system 10 includes a display screen12 or graphic user interface that portrays a network of streets as aseries of line segments, including a center line running approximatelyalong the center of each street or path, as exemplified in FIG. 1. Thetraveler can then be generally located on the digital map close to orwith regard to that center line. Such GPS-enabled personal navigationdevices, such as those manufactured by TomTom N.V. (www.tomtom.com), maybe also configured as probes to record its position at regularintervals. Such probe data points comprise a sequence of discretepositions recorded at a particular time of the day taken at intervalsof, for example, one second. Of course, other suitable devices may beused to generate probe data points including handheld devices, mobilephones, PDAs, and the like.

Maximizing energy efficiency is a universal goal. It is known, forexample, that vehicles driven with frequent start-stop type motions arevery energy inefficient due to the acceleration and deceleration aspectsof this type of driving. Conversely, maintaining a vehicle at a steadyspeed, particularly in the range of about 45-60 mph, is far more energyefficient.

Navigation devices are well known for their ability to plan a routebetween two locations in a digital map. For example, as shown in FIG. 2,a traveler originating in Detroit may select a destination of LosAngeles in the digital map and activate an algorithm to calculate aroute between the two locations. When alternate routes are possible,such route planning may be carried out on the basis of the shortestdistance between origination and destination points. Or, if links in thenetwork include associated travel time attributes, it is possible torecommend the route which indicates the shortest travel time. Othervariables may include planning a route based on points of interest, andthe like.

Some prior art devices have proposed the calculation of a route betweenorigination and destination points based on fuel economy, carbonfootprint and fuel pricing. For example, the ecoRoute™ offered by GarminLtd. uses information from a particular vehicle profile to calculate afuel consumption estimation. That is, the user inputs details abouttheir specific vehicle's fuel economy in both city and highway settings,selects a fuel type relative to the vehicle, and perhaps providesadditional details. The system algorithm then calculates fuelconsumption estimates based upon the distance to be traveled along aplanned route. One particular shortcoming of this approach is that itdoes not rely on any speed or acceleration attribute associated with thenetwork of links in a digital map database. Therefore, the ecoRoutingfunction is not particularly useful as a representative planning tool.Thus, in referring then to the example of FIG. 2, a driver wishing totravel between Detroit and Los Angeles is not able to intelligentlyassess the most economical route to travel. Furthermore, programs likethe ecoRoute™ require some burdensome user interaction with thenavigation device and user knowledge about the vehicle characteristics,fuel prices, etc.

As suggested previously, it is known to take probe data points fromlow-cost positioning systems in handheld devices and mobile phones withintegrated GPS functionality for the purpose of incrementally learning amap using certain clustering technologies. The input to be processedconsists of recorded GPS traces, perhaps in the form of a standard ASCIIstream or binary file. The output may be a road map in the form of adirected graph with nodes and links associated with travel timeinformation. The probe data, which creates the nodes or probe positionsat regular intervals, can be transmitted to a collection service orother map making or data analysis service. Through this method, whereinlarge populations of probe data are analyzed, road geometry can beinferred and other features and attributes derived by appropriatealgorithms.

FIG. 3 is a representative example of raw probe data collected over aperiod of days from a downtown, city-center area of Ottawa, Canada. Fromthis raw probe data, even an untrained eye can begin to discern roadgeometries. Each data point represented in the illustration of FIG. 3includes information as to the particular time of day that the datapoint was recorded. Thus, while FIG. 3 depicts only longitudinally andlaterally dispersed position data, the recorded data also provides atime stamp for each position. Furthermore, each individual probe maycreate a trace which can be analyzed for travel speeds, accelerations,stops, and the like.

Traditional routing methods use maximum speed limits as exist along roadsegments to calculate travel time estimates, however in practice speedlimit information is not accurate because these speeds are not alwayspossible at various times of the day. Speed profiles have been derivedby intensively processing this probe data to create average trafficspeeds for each road segment, i.e., for each section of road in thedigital map, for different time slots or times of the day. See, forexample, the TomTom IQ Routes™ product.

The IQ Routes™ product uses anonymous probe data to discover actualpatterns in driving speeds. Typically, route calculations before IQRoutes used 0.85% of the maximum speed limit in its calculation—IQRoutes by contrast uses the speeds actually driven on those roads.(Alternatively, a likely speed value can be derived from the roadclassification. E.g. when legal speed limits are not available.) Thisdata is applied to a profile model and patterns in the road speeds areidentified in time spans (e.g., 5 minute increments) throughout the day.The speed profiles are applied to the road segments, building up anaccurate picture of speeds using historical data. All of these speedprofiles are added to the existing IQ Routes data built into the mapstored in the navigation device 10, to make it even more accurate anduseful for premium routing and travel time estimates. Speed profilestherefore represent a continuous or semi-continuous averaged speeddistribution of vehicles derived from probe information, driving alongthe same section of the road and direction. Speed profiles reflect speedvariations per segment per time interval, but are not longitudinallydistributed in the sense that they do not describe velocity variationsalong the length of a link or road segment. This information can be usedby a navigation system as a cost factor in connection with calculatingoptimal routes and providing travel/arrival time estimates.

While very useful, these prior art techniques do not provide anyindication of the most efficient route between two locations representedin a digital map. Therefore, there is a need to create new and improvedmethods for computing routes between an origin and destination locationwhich provides the most energy efficient strategy, and which accountsfor real life conditions including both static and dynamic elements.Static elements may include features that affect traffic speed includingfor example sharp bends in the road, traffic controls, and othermeasures that affect traffic speed as a matter of geometry. Dynamicelements include traffic volumes which fluctuate during workdays withlocal rush hour conditions, and are affected by weekend travel, holidaysand the like. There is also a need to create new and improved data thatcan be used in connection with a digital map, either as a separateinterfacing database or as data augmented directly into an existing mapdatabase, to enable traffic modeling applications.

SUMMARY OF THE INVENTION

The invention provides a method for creating Longitudinal Speed Profile(LSP) data useful for various traffic modeling applications. Probe datais collected from a plurality of probes traversing a road segment in theform of vehicular traffic flow. Each probe develops a respective probetrace comprising a sequence of discrete probe positions recorded at aparticular time of day. Daily time spans are established, e.g., everyfive minutes, and the probe data is bundled for each time span. Theprobe data is then utilized to obtain Longitudinal Speed Profiles (LSPs)for vehicles traversing the road segment during each time span. TheseLongitudinal Speed Profiles (LSPs) are then associated with therespective road segment and either stored in a stand-alone database oradded to an existing digital map as a data layer.

The invention also contemplates a method for computing an energyefficient route between an origin location and a destination location insituations where a digital map includes a network of road segments orlinks extending between the origin and destination locations. Probe datais collected from probes traversing the links and then bundled andprocessed to obtain the Longitudinal Speed Profiles (LSPs) for each timespan. Using these Longitudinal Speed Profiles (LSPs), an energy cost iscalculated for at least one direction of travel supported by the linkduring each time span, so that a route can be planned between the originand destination by analyzing the energy cost for alternative linkcombinations in the network and preferring those links which minimizethe average energy consumption value.

From the detailed Longitudinal Speed Profiles (LSPs) along the links asderived from probe data, a detailed energy cost along the links can becalculated in the direction of travel, and perhaps even by lane inmulti-lane roads, such as by taking the first derivative of speed overtime or acquiring specific sensor data as may be available. From thisinformation, energy cost can be introduced and used by the routingalgorithms in much the same way that current routing algorithms utilizeother cost factors like travel time or distance information. While afull calculation of energy cost requires additional parameters such asaerodynamic drag, rolling resistance and road grade data, it has beendiscovered by the applicants that an energy cost parameter can be usedin at least a basic capacity to predict or estimate energy/fuelconsumption characteristics without resorting to vehicle specificinformation such as mass, frontal area, aerodynamic drag and the like.Therefore, while these other parameters can be useful in providing amore accurate energy cost for each link in the network, it is at themost basic level sufficient to utilize only an energy cost derived fromthe Longitudinal Speed Profiles (LSPs) and then using this energy costinformation to plan out a route between two points in a digital map.

It is known that the fuel and/or energy economic is very much dependanton the number of accelerations/decelerations to the total distance to betravelled and also on the vehicle speed. During every acceleration, theengine (or motor in electric vehicle applications) generates moreexcessive heat, which means the power generated by the fuel/energyconsumed is transferred less efficient into the mechanical motion and athigher speed the energy consumption is higher due to factors like airresistance. During deceleration by the application of brakes, kineticenergy is converted to heat and rejected to atmosphere. Traditionalbrakes do not recover this energy, while electric motors may recoversome energy.

This invention allows the user to plan routes containing lessacceleration/deceleration points on the route and in addition to thatpossible lower travel speeds, thus allowing the engines to work in moreefficient (closer to steady RPM) mode, which will deliver the wantedenergy economy and also decrease pollution. Navigation systems operatingroute planning software can have an option to enter the time one canspend driving a particular optional route and see how “greener” theroute will be for every time setting. Or, the person can enter theparameter of how much longer in % to the fastest time the more energyefficient route is allowed to be (as an example of one possible timesetting).

A distinct advantage of this invention for planning an eco-friendlyroute does not necessarily require any vehicle-specific information toderive useful results, although more accurate computations can be madewith the addition of vehicle-specific information. Thus, it is notabsolutely necessary that the navigator know the type of vehicle theuser has and what are the prices of the fuel in the nearby refuelingstations. The proposed method of route planning thus takes advantage ofgeneric knowledge about the efficiency of all vehicle engines/motors(that is, to drive with the fewest number ofaccelerations/decelerations) to deliver more economical routes. To thisend, this method is also applicable to routing services occurringoff-board or being retrieved over the web, such as on mapping androuting web sites used by internet users.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and advantages of the present invention willbecome more readily appreciated when considered in connection with thefollowing detailed description and appended drawings, wherein:

FIG. 1 is an exemplary view of a portable navigation system according toone embodiment of the subject invention including a display screen forpresenting map data information and including a computer readable mediumhaving navigation software recorded thereon;

FIG. 2 is a sample portion of a digital map depicting alternative routesbetween Detroit and Los Angeles which can be separately assessed forfuel economy prediction;

FIG. 3 is an example of raw probe data reflecting latitudinal andlongitudinal positions (i.e., relative to road centreline) collectedfrom a downtown, city-center area of Ottawa, Canada;

FIG. 4 is a flow diagram describing the derivation of a Raw Road DesignSpeed Limit (RRDSL) and/or a Legal Raw Road Design Speed Limit (LRRDSL)from probe data, together with the creation of Longitudinal SpeedProfiles (LSP) from probe data;

FIG. 5 is a chart showing the derived Longitudinal Speed Profiles (LSPs)for a particular road segment (AB), for a particular direction oftravel, during different time spans, in this example in 30-minuteincrements;

FIG. 6 is a diagram representing the posted speed limit for severalconsecutive road segments (AB-IJ), together with the RRDSL (16) for thesame road segments;

FIG. 7 is a diagram as in FIG. 6 but showing also the LRRDSL (17) forthe same road segments (AB-IJ);

FIG. 8 is a flow diagram describing the derivation of an OptimumLongitudinal Speed Profile (OLSP) from either the RRDSL or LRRDSL, for aparticular direction of travel;

FIG. 9 is a diagram as in FIG. 7 but showing also the OLSP for the sameroad segments (AB-IJ);

FIG. 10 is a simplified longitudinal speed diagram for a road segmentAB, showing both the RRDSL and OLSP, with energy savings represented bythe OLSP being shown as an energy difference between the curves;

FIG. 11 describes one method for determining an energy cost for a roadsegment for a particular time span; and

FIG. 12 illustrates another method for determining an energy cost for aroad segment for a particular time span.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Referring to the Figures, wherein like numerals indicate like orcorresponding parts throughout the several views, this inventionpertains to position reading devices, navigation systems, ADAS systemswith GNSS (Global Navigation Satellite System), and the digital mapsused by navigation systems. This invention is therefore applicable toall kinds of navigation systems, position reading devices and GNSSenabled units including, but not limited to, handheld devices, PDAs,mobile telephones with navigation software, and in-car navigationsystems operating as removable or built-in devices. The invention can beimplemented in any type of standard navigation system available on themarket, on mapping and navigation web sites/servers as far as energyefficient route planning is concerned, as well as suitable systems whichmay be developed in the future.

The navigation-capable device typically includes a computer readablemedium having navigation software recorded thereon. A microprocessorassociated with the device may be programmed to provisionally match thenavigation device to a particular road segment in a digital map and thento make an assessment whether the provisional match is reliable. If notreliable, the system may rely on other techniques to determine theposition of the navigation-capable device, such an auxiliary inertialguidance system for example. Such inertial guidance systems may alsoinclude other features such as a DMI (Distance Measurement Instrument),which is a form of odometer for measuring the distance traveled by thevehicle through the number of rotations of one or more wheels. Inertialmeasurement units (IMUS) may be implemented with gyro units arranged tomeasure rotational accelerations, with suitable accelerometers arrangedto measure translational accelerations. The processor inside thenavigation device may be further connected to a receiver of broadbandinformation, a digital communication network and/or a cellular network.

A microprocessor of the type provided with the navigation deviceaccording to this invention may comprise a processor carrying outarithmetic operations. A processor is usually connected to a pluralityof memory components including a hard disk, read only memory,electrically erasable programmable read only memory, and random accessmemory. However, not all of these memory types may be required. Theprocessor is typically connected to a feature for inputtinginstructions, data or the like by a user in the form of a keyboard,touch screen and/or voice converter.

The processor may further be connected to a communication network via awireless connection, for instance the public switch telephone network, alocal area network, a wide area network, the Internet or the like bymeans of a suitable input/output device. In this mode, the processor maybe arranged to communicate as a transmitter with other communicationdevices through the network. As such, the navigation-capable device maytransmit its coordinates, data and time stamps to an appropriatecollection service and/or to a traffic service center.

As stated previously, it is known that improved fuel efficiency can beachieved by maintaining a constant, optimal vehicle speed. As a rule ofthumb, this constant vehicle speed may be approximately 45-60 mph,however that range may vary from one vehicle type to another, as well asbeing influenced by environmental conditions, road geographies, and thelike. It is further known that various road characteristics such assharp turns, speed bumps, lane expansions/consolidations, trafficcontrols and other features can influence the ability to safely travelat a constant speed along a particular segment. For this reason, thesubject invention provides new, detailed map content to be used inconnection with the navigation software applications to provide optimalenergy-efficient driving speed recommendations.

A Raw Road Design Speed Limit (RRDSL) may be derived from the collectedprobe data, according to the steps outlined in FIG. 4. With regard todetermining an RRDSL, the first step is to identify the time frameduring which free flow traffic (no congestion) occurs. Once this freeflow time span is known, the probe data for that time span is bundled,and then statistically analyzed to derive the speed at every point alongthe link, i.e., the road segment. Alternatively to selecting an optimaltime span, the probe data can be analyzed to identify the higher probespeeds regardless of the time span. This process of deriving the speedat every point along the link is carried out for every road segment (oras many segments as practical. The RRDSL may be associated with itsrespective segment as an attribute. In this manner, the digital map isaugmented with the RRDSL attribute. Additionally, an attributereflecting the averaged or longitudinal statistical information of theprobe data along the road element (e.g. standard deviation) can be addedto the digital map.

The RRDSL represents the longitudinally variable (vehicle) speed at anylocation along a road section in one direction where no obstructions totraffic are observed. The RRDSL for each road segment is either takenfrom probe data at a time span where free flow traffic conditions areobserved, or taken from probe data possessing the highest speedsregardless of the time span. For many road segments, free flowconditions will occur in the early morning hours when the fewest numberof vehicles are traveling the roads. Thus, a speed profile (like thatobtained from the TomTom IQ Routes™ product) taken at the time of theleast traffic congestion may be somewhat similar to the RRDSL for agiven road segment, but the IQ Routes™ speed profile will be a singleaverage speed for the entire road segment whereas the RRDSL willtypically have speed changes along the length of the road segment.

The RRDSL is thus characteristic for specific locations along a roadlink and renders all effects which physically restrict the vehicles fromgoing faster. As the information is derived from vehicle probes andreflects true driving, it may at times exceed the legal speedrestriction. When the RRDSL is represented along a road in a continuousor semi continuous way, one could call it an undisturbed speed which,when driven, is influenced primarily by the physical attributes of theroad segment (e.g., its geometry) and the posted speed limits (if any).The RRDSL can therefore be classified an attribute of a road segment; itdoes not vary over time of day. Only when road construction changes orroad furniture is changed, or probe statistics change, is the RRDSLexpected to change. As an attribute, it is possible to consider futureapplications of this concept in which, for example, a percentage of thestored RRDSL could be taken in case weather/surface conditions areknown. As probe data content and resolution improvements are available,lane and/or vehicle category dependencies may be represented in theRRDSL. For example, with sufficient data content, the RRDSL may reflectregulatory situations such as higher speed limit on left lane or lowerspeed limit for commercial vehicles, etc. That is, the RRDSL canoptionally be dependent on the specific vehicle type, or moregeneralized in vehicle categories (e.g. Powered Two Wheeler, HeavyTruck, Light Commercial Vehicle or Passenger car). The RRDSL isparticularly useful for Advanced Driver Assistance (ADAS) and otherdriving control purposes.

Accordingly, the RRDSL is derived from selected and filtered probe datawhich has been collected during periods of time when traffic flow is ator near its lowest for a particular road segment, i.e., at free flowconditions, or which has demonstrated the highest speeds. The RRDSL16 isa function of the longitudinal profile, based on position along a roadsection and of the travel-based direction profile (i.e., f(p, d)). Onemight possibly consider the RRDSL16 also a function a time-intervalbased profile as well as of a lane-specific profile (i.e., f(p, d, t,l)) if one wishes to accommodate longer-term changes such asconstructions, change in road furniture, and the like.

FIG. 5 shows exemplary Longitudinal Speed Profiles (LSPs) derived fromprobe data (like that of FIG. 3) for a hypothetical road segment (AB),for a particular direction of travel, during consecutive 30-minute timespans. Unlike the traditional speed profiles taught, for example, by theIQ Routes™ product, these profiles represent longitudinally (i.e., inthe direction of the road centreline) varying average speeddistributions of vehicles derived from probe information, driving alongthe same section of the road and direction. These LSPs describe velocityvariations along the length of a link or road segment for a specifiedtime span. For the time span(s) which coincide with free flow trafficconditions, the LSP will be equivalent to the RRDSL 16. Once derivedfrom the collected probe data, the LSPs are associated with therespective road segment and either stored in a stand-alone database oradded to an existing digital map as a data layer.

FIG. 6 is a sample chart depicting consecutive road segments AB, BC, . .. IJ. Each road segment has a legal speed limit which is recorded in thedigital map as an attribute. These speed limits are represented by theheavy, horizontal lines occurring at 30, 50 and 75 km/h. Broken line 16represents the RRDSL for the same road segments (AB, BC, . . . IJ) whichhas been developed by bundling probe data recorded during an optimaltime span (e.g., 0200-0230) and then averaging the results. Variationsin the RRDSL 16 speeds can be attributed to features and geometries andattributes associated with each road segment, as suggested along theupper margin of the illustration. Features such as good physicalvisibility e.g. no obstructions or objects in line of sight, andexpansion of roadway from single to dual carriageways are shown toresult in velocity increases in the RRDSL 16 speed, whereas featuressuch as speed bumps, sharp turns and speed cameras mark declines in theRRDSL 16 speed, in many instances below the legal speed limit. The RRDSL16 can vary even within the context of a single road segment, isassociated in the digital map with the particular road segments and madeavailable to navigation-capable devices which utilize the digital map inan interactive manner. As suggested earlier, one embodiment of thisinvention contemplates a target driving speed derived by considering thedynamic environmental situation (e.g., degraded road surface conditionsor poor weather) and calculating a fraction of the RRDSL.

The RRDSL 16 can be attributed to its associated road segment in adigital map database in various ways. For some examples, an RRDSL 16 canbe represented and stored as a parametric curve as a function ofdistance, or perhaps as a set of discrete optimal speeds between whichto linearly interpolate, or normalized variations (percentages) aboveand below a legal speed limit/artificial threshold, to name a fewpossibilities. Those of skill in the field of digital map databaseconstruction and implementation will readily appreciate these andpossible other suitable techniques how to represent and store an RRDSL16 in a map database. Furthermore, various averages can be stored in adigital map, and provided for different types of vehicles. In the caseof multi-lane road segments, e.g., dual carriageways, variations in suchprofiles can also be lane dependent. In addition, a sub attributerepresenting the statistical signal of the RRDSL 16, e.g. in the form ofa standard deviation, can be stored in the map as well. Either as anaverage value, or as a longitudinal varying representation along theroad element.

Once the RRDSL 16 has been determined, and then associated with roadsegments in a digital map, a driver operating with a navigation-capabledevice is able to continually compare their current speed (derived fromsuccessive GPS coordinates of the current time, or optionally derivedfrom in-car sensor data) with the undisturbed speeds represented by theRRDSL 16 for the particular road segment. In the event of bad weather,environmental or surface conditions, a percentage of the RRDSL 16 may beused instead of the actual derived speeds which is proportional to thedegraded driving conditions. The navigation device then providessuccessive instructions or suggestions to the driver in audible, visualand/or haptic form, so that the driver might alter their driving speedto match or more closely mimic the target speeds along the road segmenton which the vehicle is currently traveling. As a result, the driver canexpect to optimize their use of fuel in the most realistic mannerpossible, because the free flow conditions (upon with the RRDSL 16 wasderived) represent the closest to steady-speed operation taking intoaccount the practical considerations of road geometry and otherreal-world factors that influence driving speeds. This not only reducesoperating costs of the vehicle, but also reduces vehicle emissions tothe atmosphere and can improve driver comfort by reducing driver stressand fatigue. In more advanced systems, including the so-called ADASapplications which partly automate or take over driving tasks, thenavigation device may even take an active role in conforming the currentspeed to the RRDSL 16 speeds. Thus, in order to achieve high energyconservation, sensory signals (e.g., audible, visual and/or haptic) willbe activated by the navigation device if the current, instantaneousspeed of the carrying vehicle exceeds the RRDSL 16 target speed by somethreshold value. For example, a threshold value of ±5 km/h, or apercentage (e.g., 10%) may be established.

As shown in FIG. 6, it is foreseeable that, in many real lifesituations, the RRDSL16 will at times exceed the posted legal speedlimits for a particular road segment. It is possible, indeed perhapseven preferable, therefore to reduce the target speeds of the RRDSL 16to the legal speed limit whenever it exceeds the established speed limitat any point along the particular road segment. Thus, as shown in FIG.6, the target speeds may be capped at each point where it rises abovethe local legal speed limit, resulting in a so-called Legal Raw RoadDesign Speed Limit (LRRDSL) 17. It is to be understood, however, thatuse of the term “legal” in this context does not preclude strategiclimitation of the RRDSL speeds for reasons other than compliance withlocal speed regulations. For example, road segments in somejurisdictions may not impose any upper speed limit. This is sometimesthe case along sections of the Autobahn in Germany for example. Applyingprinciples of this invention to such unrestricted sections of roadwaymay result in a distribution of probe speeds with a very large spread,e.g., real speeds between 100 kph and 200 kph. In such cases, it may beadvisable to impose an artificial maximum threshold that is mindful offuel economy statistics. Thus, for example, in road segments withoutlegal speed limits, an artificial maximum threshold of 110 kph might beestablished, and used to limit the LRRDSL 17 where ever it exceeds theartificial threshold.

As will be appreciated by reference to the RRDSL 16 and LRRDSL 17 curvesas shown in FIG. 7, sharp changes may sometimes occur in the targetspeed. Sharp target speed increases require heavy acceleration, whereassharp target speed decreases require strong deceleration. To improveenergy efficiency amid sharp changes in the target speed, an OptimalLongitudinal Speed Profile (OLSP) 18 may be introduced. The OLSP 18 is afluent profile that reflects driving without too manyaccelerations/decelerations and thus represents minimal energy losses.

The flow chart of FIG. 8 describes two alternative approaches toderiving the OLSP 18. In one approach, the OLSP 18 is derived on thebasis of kinetic energy simulations for various vehicle types orcategories. In this case, the OLSP 18 is simply attributed to therespective road segment in the digital map. Alternatively, the OLSP 18can be computed dynamically, i.e., on the fly, on the basis of dataspecific to the vehicle. Regardless of the method used, the target speeddictated by the OLSP attribute 18 is then used as the standard againstwhich current vehicle speed is compared. As shown, an optional step“Dynamic real time parameter or coefficient (e.g. weather, road surfaceor visibility)” may feed into the step “Navigation Device or In-vehicleDriver Assist system to monitor current speed and compare with OLSP onroad segment ahead of current position.” This enhanced real time OLSP 18can alternatively be applied to the RRDSL 16 or the LRRDSL 17. Thedynamic parameter could be manifested as an absolute delta speed, or arelative speed differential (i.e., a percentage) or speed that iscategorised/indexed (e.g., low/med/high) to the OLSP 18 (or the RRDSL 16or LRRDSL 17). This dynamic parameter may be provided to the navigationdevice 10 so that the system can calculate navigation and drivingguidance instructions taking into account the real time dynamicsituation, relative to the free flow target speed indicated by the OLSP18 (or the RRDSL 16 or LRRDSL 17). In addition, information can beprovided to the navigation device 10 identifying the cause of the changeof the parameter (e.g. congestion, partly road/lane closure, road works,road surface conditions, visibility, weather, events and incidents,etc.)

Ideally, the comparison is proactive, in the sense that it is made onthe road segment ahead of the current position so that an appropriatesensory signal (e.g., visual, sound, haptic, etc.) can be issued,considered by the driver and reacted upon in time with the movement ofthe vehicle. FIG. 9 shows the diagram of FIG. 7 superimposed with anOLSP 18. The OLSP 18, like the RRDSL 16, is also a function of thelongitudinal profile, based on position along a road section and of thetravel-based direction (i.e., f(p, d)). It may also be a function ofvehicle category (passenger car, bus/truck, powered two-wheeler), and isalso preferably, but not necessarily, a function of a regulationdependency (like the LRRDSL 17). Apart from being efficient due tominimal accelerations, minimizing the energy spend over the road segmentalso reflects a speed which will be close to the legal speed limit onthe higher road classes. In fact, vehicle manufacturers typicallyoptimize the power trains of their vehicles to be most efficient between85-95% of their top speed, which nearly always reflects the legal speedor speed restrictions in the region. Stated simply, the OLSP 18 is acontinuous or semi-continuous averaged speed distribution of vehiclesdriving along the same road and direction, considering the RRDSL 16 orthe LRRDSL 17, and minimizing the number of accelerations/decelerationsbut keeping close to the RRDSL 16 (or LRRDSL 17) when no junctions areapproached. Again, the term “longitudinal” appearing in the OLSP refersto the (semi) continuous description of this information along a road'saxis. The highest average speed profiles are represented by the RRDSL16. Using the RRDSL 16, or rather the trimmed version LRRDSL 17, theOLSP 18 can be calculated by investigating the changes in energyinvolved in the system.

Computing the OLSP 18 respects the difference between the need foracceleration changes to be as small as possible, and keeping a fluentprofile whilst keeping the vehicle in a speed zone for which themanufacturer optimized the functioning of its power train. Those ofskill in the field will appreciate various methods to derive the OLSP 18from the LRRDSL 17 (or if preferred from the RRDSL 16). With regards toderivation of the optimal acceleration and decoration strategy, thereexist some models in the state of the art that can be well used for thispurpose. In one approach, boundaries are set on acceleration values.See, for example, the Optimal Velocity Profile Generation for GivenAcceleration Limits described at:http://soliton.ae.gatech.edu/people/ptsiotra/Papers/acc05b.pdf. Inanother approach, mathematical models can be constructed to predictenergy costs for motor vehicles along roads. These models are fed withvehicle characteristics and a specific longitudinal speed profile.Linked to the energy estimation models are those which predict fuel costand emission values. Modeling examples include PAMVEC, ARFCOM, andARTEMIS. Details about the PAMVEC model can be found at:http://www.itee.uq.edu.au/˜serl/_pamvec/PhD_Thesis_AGS_Chap3.pdf.Details about the ARFCOM model can be found at:http://www.transport-links.org/transport_links/filearea/publications/1_(—)773_PA3639.pdf.Details about the ARTEMIS model can be found at:http://www.epa.gov/ttn/chief/conference/ei18/session6/andre.pdf.

The energy difference optimized by the OLSP 18 in relation to the RRDSL16 is represented in FIG. 10 by the shaded area. The energy saved byobserving the OLSP 18 rather than the RRDSL 16 is proportional to theavailable energy conservation. Individual vehicles driving according tothe OLSP 18 will be using less fuel. The surrounding traffic will beinfluenced with the behaviour of the vehicles driving according to therecommendations based on the OLSP 18 (or an OLSP 18 enhanced with adynamic parameter). Thus, the OLSP 18 will not only impact the vehiclesactually using the information but will also have a significant andbeneficial secondary impact on surrounding traffic.

Personal navigation devices 10 like those described above areparticularly efficient at comparing many different routes between anorigination and destination location and determining the best possibleor optimum route, as shown in FIG. 2. Typically, such route planningalgorithms consider a so-called cost attribute associated with eachpossible link in the network which is sought to be minimized ormaximized by the navigation/route planning software. This type of routeplanning technique is well known for determining the fastest, shortestor other cost criteria route between two points. However, until nowthere has not been a convenient method by which the most energyefficient route can be calculated between two points and then offered toa user in preference to a route calculated according to some traditionalbasis. Using the technique of calculating the LSPs it is possible toderive also an energy cost for each link in the network for each timespan. Because the LSP at free flow conditions (i.e., at the time spanwith the highest observed speed distributions) is equivalent to theRRDSL 16, a special case of the energy cost at the free flow time spancan be determined by reference to the RRDSL 16. It follows, therefore,that a useful version of the energy cost for free flow conditionsspecifically can also be determined by reference to either the LRRDSL 17or the OLSP 18.

FIG. 11 shows one method by which a longitudinally distributed energycost can be determined for any road segment, in this example roadsegment AB. In this figure, Energy Cost is calculated on the basis ofthe area under the LSP for the road segment AB for a time span. However,it may be useful in some cases to simply calculate a singe Energy Costfor the road segment AB which is not time dependent. In these specialsituations, the LRRDSL 17 may be used, as it represents the LSP forsegment AB at the free flow time span. Or perhaps, a (pragmatically)optimal, time independent Energy Cost could be calculated on the basisof the OLSP 18, which also corresponds to the LSP for segment AB at thefree flow time span. Although not preferred, it is likewise possiblealso to derive a time independent Energy Cost from the RRDSL 16.

The energy cost is preferably indexed to the time intervals for theLSPs, e.g., every five minutes or every half hour as in FIG. 5.Therefore, this energy cost would be derived from real trafficinformation as collected from the probe data, and thus incorporatedynamic aspects as well as static aspects attributable to roadgeometries and the like. However, it may be useful in some routingapplications to consider only the special case of energy cost duringfree flow conditions, which can be derived from any of the RRDSL 16,LRRDSL 17 or OLSP 18 due to their correspondence with the LSP for thefree flow time span.

The energy cost can be represented as cost information and associateddirectly with each link, i.e., with each segment between two nodes in adigital map, and thereby represent a cost criteria related to energyconsumption over that link. Thus, the energy cost is calculated at leastfrom the speed and acceleration profiles (i.e., LSPs) obtained fromprobe data and is relative to other links on the map. The averagevelocity profile and average acceleration profile are particularlyrelevant in view of the parametric approach to modeling vehicle energyconsumption founded upon the well-known road load equation:

$\begin{matrix}{P_{road} = {P_{aero} + P_{roll} + P_{accel} + P_{grade}}} \\{= {{\frac{1}{2}\rho \; C_{D}{Av}^{3}} + {C_{RR}m_{total}{gv}} + {k_{m}m_{total}{av}} + {m_{total}{gZv}}}}\end{matrix}$

Where:

-   -   P_(road) is the road load power (W),    -   v is the vehicle speed (m/s),    -   a is the vehicle acceleration (m/s₂),    -   ρ is the density of air (˜1.2 kg/m₃),    -   C_(D) is the aerodynamic drag coefficient,    -   A is the frontal area (m₂),    -   C_(RR) is the rolling resistance coefficient,    -   m_(total) is the total vehicle mass (kg),    -   g is the gravitational acceleration (9.81 m/s₂),    -   Z is the road gradient (%) and    -   k_(m) is a factor to account for the rotational inertia of the        power train (Plotkin et al. (2001) use a value of k_(m)=1.1        while Moore (1996) uses a value of k_(m)=1.2).

In this equation, acceleration loads (P_(accel)) are typically moreheavily weighted than resistance due to aerodynamic drag (P_(aero)) orresistance due to rolling (P_(roll)) or resistance due to gravitationforces (P_(grade)). As shown in the equation above, the load due toacceleration (P_(accel)) includes the product of acceleration timesvelocity (av). Thus, by establishing the speed-acceleration index (av)as a parameter used in the average energy value and determined for eachlink in the network, a substantially reliable prediction or estimationcan be made on a universal basis as to the energy required by anyvehicle to traverse the link. In other words, while the specific amountof energy will vary from one vehicle to the next depending upon a greatmany conditions and variables, the speed-acceleration index will serveas a useful estimation tool so that routing algorithms can apply atleast a simplified version of the average energy value as a cost andchoose the best route between two locations in a digital map byattempting to minimize the energy loss.

An alternative technique for determining energy cost for a road segment(hypothetically AB) is to take the first derivative of the LSP, whichmay be characterized as an acceleration profile. Using this accelerationprofile, it is possible to keep track of the number of accelerations anddecelerations above a set threshold. This count can then be assigned toa road segment. Such an acceleration profile would provide a simplifiedwas to store information that in turn can be used to compute the energycost. A routing algorithm would favor segments with high speed. On ahigher level, the routing algorithm needs to identify chains of roadsegments with the overall minimum energy loss. This information can beused in navigation systems to select the least energy consuming route.Thus, the LSPs (or the LRRDSL 17, OLSP 18, or even the RRDSL 16, fortime independent applications) can be used as a predictive or routingfunction to find an economical route by considering it in routingalgorithms. Furthermore, the OLSP 18 can be used in conjunction with asuitable navigation device 10 to provide an instantaneous performanceindicator by offering a reference signal to which real time comparisonscan be made so as to advise the driver.

In FIG. 12, yet another method to compute the Energy Cost is presentedas a velocity times acceleration index is plotted along the length ofthe link for a particular driving direction. As in the precedingalternate technique to determine the energy cost by creating anacceleration index, this method may also keep track of the number ofpeaks extending past a threshold. In FIG. 12, the threshold is shown ashorizontal broken lines spaced equidistant from the x-axis, with therespective peaks extending past the threshold shaded. When theacceleration is null, or zero (i.e., constant velocity), thespeed-acceleration index will be null/zero on the graph. Positive or topspeed peaks will be created as the speed-acceleration index resides inpositive territory, whereas negative or minimum speed peaks are shownduring deceleration modes with negative values presenting. Thus, theenergy cost can be complemented with the number of top and/or minimumpeaks that exist along that link. This may be simply represented with anabsolute number or expressed by some other efficient method. The numbercould be calculated using statistics on speed-acceleration indicesderived from probe data on the link, for example. By this method, it ispossible to conclude that an energy efficient routing application mayinclude routing algorithms which compare the number of peaks and themagnitude of peaks along the speed-acceleration index between differentlinks. Such a value could be automatically calculated and added to adigital map for each link complementary to the speed profiles andacceleration profiles provided in the time span divisions.

Accordingly, the energy cost associated with a particular road segmentor link can be computed using many different techniques, including butnot limited to those described here. Once computed, the energy cost maybe compared on a link-by-link basis in the digital map to evaluate howmuch disturbance there is on any particular route. (Refer again to FIG.2.) Once a preferred route has been established, the navigation system10 can provide navigation assistance to the driver along that route toobtain the further improved fuel economy by observing the OLSP 18described above. Thus, not only will the driver be able to calculate themost energy efficient route between two points, the driver will also beassisted to drive in an even more efficient manner along the route whichtakes into account geometric and other static road features along theentire route.

In a preferred embodiment, computation of an energy efficient route isusing an index of energy costs which is based on LSPs as this takes intoaccount the time-dependent nature of speed distribution along roadsegments. In another embodiment, energy efficient routing uses energycosts based on OLSP 18, or alternatively RRDSL 16 or LRRDSL 17, eitherof which is not a time-dependent LSP but represents ideal (free-flow)traffic conditions. This allows achieving at least a basic level ofenergy efficient routing, in that alternate routes can still be comparedon basis of overall energy cost. In this way, the lowest possibleoverall energy cost for a desired route can be established. Depending onthe choice for RRDSL 16, LRRDSL 17, or OLSP 18, three differentcharacteristics of lowest possible overall energy cost are conceivable,as will be appreciated by a person skilled in the art. In yet anotherembodiment, energy cost based on LPS and energy cost based RRDSL 16,LRRDSL 17, or OLSP 18 can be considered in a combined view, in that theenergy cost determined through LSPs is compared to the lowest possibleoverall energy cost (i.e. energy cost based on RRDSL 16, LRRDSL 17, orOLSP 18). Information about the efficiency comparison, expressed asratio, percentage, normalized score or other suitable measure, may berecorded or presented to the user (such as an actual efficiency score).In a further embodiment, a user may preset an efficiency comparisontarget as part of the route planning exercise.

Thus, according to the principles of this invention, detailed speed andacceleration information can be calculated along each link in a networkas derived from probe data in the form of time independent attributes ofRRDSL 16, LRRDSL 17, and OLSP 18 or in the form of the time-dependantLSPs. Using one (or more) of these references, the energy consumptionalong the road can be approximated using various alternative techniqueson the basis of one or more of these derived values. In fact, the energyconsumption along a road can be even more accurately approximated ifadditional parameters are known such as: vehicle mass, air density,aerodynamic drag, frontal area, rolling resistance, gravitationalacceleration, road gradient and rotational inertia of the power train.Calculating an energy cost may also include specialization by vehiclecategory, such as separate categories for trucks, passenger cars, buses,etc. The vehicle category-specific energy cost may then be derived fromprobe data bundled by vehicle category. In other words, probe dataacquired from bus transits will be used to calculate an energy cost thatis specific to buses, and so forth.

In conditions where some or all of these parameters are known, it may bepreferable to aggregate this information into a single index which canbe attributed to a link in a digital map database per travel directionor even per lane. This value can then be considered in the routingalgorithm to prefer those links which most precisely minimize the energyconsumption. These values can be computed using the well-known roadequation set forth above in connection with the described parametricapproach.

As stated previously, acceleration is the relative component to captureand assess the vehicle energy consumption. Changes in acceleration arequantified into a speed-acceleration index which is the product ofvehicle acceleration and vehicle speed along the road link. One way toquantify the acceleration impact over a road link is to calculate thearea enclosed by the speed-times-acceleration function, both for thearea with positive and with negative acceleration. This is describedwith reference to FIG. 12 in which positive accelerations are shownabove the null/zero line and negative accelerations below the null/zeroline. Another, additional value that may be stored as an attribute ofthe road link can be the sum of the number of acceleration energy peaksabove (and below) a predefined threshold, as represented by the brokenhorizontal lines in FIG. 12. (This can also be normalized over thelength of the road link.) This would give a count of positive andnegative acceleration energy peaks which, as stated previously, may beused to more fully develop an energy cost and provide an efficientestimating tool.

Aerodynamic resistance is also a valuable parameter. Here, the cube ofvehicle velocity, as available in the detailed probe speed profiles, isof importance. One approach may be to quantify the energy consumptiondue to aerodynamic drag using thresholds (e.g., 30 km/h, 50 km/h, 90km/h, 120 km/h) and to measure the length in meters for each sectiondelimited by the thresholds. For example, a road of 1 km length, 250 mis in the 30-50 km/h, 500 m above the 120 km/h, and 250 m in the 50-90km/h.

Rolling resistance is another parameter. The vehicle energy consumptionis governed by the vehicle speed as described by the energy loadequation stated previously. The quantification of this energy may beaccomplished by adopting a similar approach as above—namely summing thelength of the stretches of road where the vehicle speed falls within aspecific category. Other parameters to assess the rolling resistanceparameter can be to estimate the rolling resistance coefficient(C_(rr)), assume vehicle mass per class, or the like.

Loads due to road gradient are another factor. Vehicle mass may beassumed or given, and gravity is known. Therefore, the decisiveparameters to quantify the energy consumption due to road gradient arethe product of the road gradient and the velocity. The road gradient isor will be available in most digital map databases. The product of speedand road gradient along the road the vehicle travels results in a signalsimilar to the speed-acceleration index. Thus, a similar positive andnegative peaks calculation may prove useful. However, as a somewhatsimpler alternative, the road gradient profile can be integrated alongthe road link to obtain its height. Allowing positive height in metersand negative height in meters should suffice to enable an accuratecalculation.

Thus, a formula to calculate or estimate the energy consumption moreaccurately over each link in the map database may include any or all ofthe components mentioned above, but in all cases includes at least thespeed-acceleration index (i.e., LSPs) as defined. By these techniques,an energy efficient routing algorithm effectively makes an estimationusing the speed profiles and acceleration profiles derived from probedata so that very accurate and useful route planning and navigationassistance can be provided.

Like the OLSP 18, an acceleration index can also be attributed to itsassociated road segment in the digital map database in various ways. Forsome examples, an acceleration index can be represented and stored in amap database by approximation of the positive and negative peaks interms of their position along a link together with the respectivevertical size and horizontal width, or as a parametric curve as afunction of distance, or perhaps as a set of discrete optimal speedsbetween which to linearly interpolate, normalized over the road linklength, etc. Those of skill in the field of digital map databaseconstruction and implementation will readily appreciate these andpossible other suitable techniques how to represent and store anacceleration index in a map database.

A vehicle speed reflecting an optimal, high efficiency speed is based onlow traffic situations. Therefore, it is desirable to derive theattributes 16, 17, 18 from processing of other profiles resulting from aminimum amount of traffic. In an alternative embodiment however, theattributes 16, 17, 18 can be derived for different times spans on thebasis of historic traffic situations using the derived LSP data. Thederived attributes will preferably include accelerations anddecelerations witnessed by all vehicles and/or by specific vehicle typessuch as heavy trucks, delivery vans and the like. These attributes arepreferably derived for a particular driving direction, i.e., for eachlane of a multi-lane road segment, at a particular time span orinterval. As this data reflects driving behavior, it implicitly includesspeed adaptations caused by infrastructure (traffic lights, curvy roadsegments, speed bumps, etc.) and perhaps eventually also by expertdrivers. That is, drivers whose cars are equipped with devices toenhance fuel economy as well as drivers who have studied eco-friendlydriving styles. The emphasis of the contribution of the latter may bedetermined when the probe signal from which the speed profiles arederived will identify classes of drivers and/or vehicle characteristics.

The foregoing invention has been described in accordance with therelevant legal standards, thus the description is exemplary rather thanlimiting in nature. Variations and modifications to the disclosedembodiment may become apparent to those skilled in the art and fallwithin the scope of the invention.

1. A method for creating Longitudinal Speed Profile (LSP) data, saidmethod comprising the steps of: collecting probe data from a pluralityof probes traversing a road segment in the form of vehicular trafficflow, each probe developing a respective probe trace comprising asequence of discrete time-stamped probe positions; establishing dailytime spans; bundling probe data recorded during each time span;statistically deriving Longitudinal Speed Profiles (LSPs) from thebundled probe data, the Longitudinal Speed Profiles (LSPs) describingthe speed variations along the road segment during the respective timespans; associating the Longitudinal Speed Profiles (LSPs) with the roadsegment; and storing the Longitudinal Speed Profiles (LSPs) in a digitalmedium; said method further comprising the steps of: utilizing theLongitudinal Speed Profiles (LSPs) data to calculate an energy cost fora direction of travel supported by the link; and associating the energycost with the road segment.
 2. The method according to claim 1 whereinsaid step of statistically deriving Longitudinal Speed Profiles (LSPs)includes determining a given Longitudinal Speed Profile (LSP)individually per direction of travel along the associated road section.3. The method according to claim 2 wherein said step of statisticallyderiving Longitudinal Speed Profiles (LSPs) includes determining a givenLongitudinal Speed Profile (LSP) individually per driving lane of theassociated road section.
 4. The method according to claim 1 wherein saidstep of bundling probe data includes deriving at least one vehiclecategory-specific Longitudinal Speed Profile (LSP).
 5. The methodaccording to claim 1 further including the step of providing a digitalmap having at least one link corresponding to the road segment; and saidstep of storing the Longitudinal Speed Profiles (LSPs) includingaugmenting the digital map with a data layer containing the storedLongitudinal Speed Profiles (LSPs).
 6. (canceled)
 7. The methodaccording to claim 1 wherein the digital map includes a network of linksextending between an origin location and a destination location,calculating an energy cost for each link in the network, and furtherincluding the step of planning a route between the origin and thedestination by analyzing the energy cost for alternative links in thenetwork and preferring those links which minimize the energy cost. 8.The method according to claim 1 wherein said step of calculating anenergy cost for the link includes adding the number of top and/orminimum speed peaks on that link.
 9. The method according to claim 1wherein said step of creating an energy cost includes at least one of:(i) taking the first derivative of speed over time as derived from theprobe data (ii) taking the probe data itself which already containsmeasured speed and/or acceleration values from specific sensors; (iii)quantifying the area enclosed by the speed-times-acceleration function,both for the area with positive and with negative acceleration; (iv)deriving at least one vehicle category-specific energy cost from probedata bundled by that vehicle category; (v) calculating an aerodynamicresistance, the energy cost being proportional to the aerodynamicresistance; (vi) calculating a rolling resistance, the energy cost beingproportional to the rolling resistance; (vii) calculating a gradient,the energy cost being proportional to the gradient. 10-15. (canceled)16. The method according to claim 1 wherein said step of establishingdaily time spans includes at least one of: (i) creating unique timespans for each day of the week; and (ii) creating unique time spans fornational holidays. 17-18. (canceled)
 19. A method for computing anenergy efficient route between an origin location and a destinationlocation in a digital map, said method comprising the steps of:providing a digital map having a plurality of road segments representedtherein by a network of links corresponding to road sections in realitysupporting vehicular travel in at least one direction, the linksextending between an origin location and a destination location;collecting probe data from a plurality of probes traversing the links,each probe developing a respective probe trace comprising a sequence ofdiscrete time-stamped probe positions; establishing daily time spans;bundling probe data recorded for each link during each time span;statistically deriving Longitudinal Speed Profiles (LSPs) from thebundled probe data, the Longitudinal Speed Profiles (LSPs) describingthe speed variations along the road segment during the respective timespans; utilizing the Longitudinal Speed Profiles (LSPs) during at leastone time span to calculate an energy cost for the direction of travelsupported by the link; planning a route between the origin and thedestination by analyzing the energy cost for alternative linkcombinations in the network and preferring those links which minimizethe energy cost.
 20. The method of claim 19 further including the stepof associating each energy cost to the respective link as an attributein the digital map. 21-22. (canceled)